In the field of retail product sales, a method and system for categorizing products and optimizing the variety of products carried at a retail store based upon an analysis of retail sales data for a geographic region and a chain of stores.
Retail sales of packaged food items is a very competitive business offering a wide variety of products. For example, a single food manufacturer may produce food items based upon different food types (e.g. potato chip, corn chip, cheese puff), flavoring (e.g.. salted, Bar-B-Que, ranch dressing, Mexican spicy seasoning, etc.), package sizes (e.g.. 32 oz., 24 oz., 12 oz), and package types (e.g., bag, tin, tub, canister). A retail store must select its assortment from the wide variety of products available in the market. Preferably, the retail store would like to select the most popular goods for its assortment.
Many forces contribute to the popularity of a particular packaged food item such as marketing campaigns, the seasons of the year, and the consumer appeal in a specific geographic area. For instance, a marketing campaign directed toward publicizing a certain packaged food item could increase the demand for that food item significantly over competing goods and the prior sales performance for that food item. Further, the summer months may make one packaged food item more appealing, while colder weather may have a reverse impact on the sales of that same product. People living in different geographic areas may find certain packaged food items more appealing than others. For instance, people in the southern United States may find corn-based snack food items more appealing than the northern United States, while those people in the midwest United States may find potato-based packaged snack food items more appetizing.
The packaged food items with greater popularity will inevitably sell much faster than packaged food items of less popularity. Greater sales usually translates into greater profits. Of all products in the market, the most popular products only comprise a small percentage of the total number of items available. In fact, it has been estimated that twenty percent (20%) of the most popular food items comprise approximately eighty percent (80%) of all sales in that category of food products. Moreover, fifty to sixty percent (50%-60%) of the least popular packaged food items comprise approximately ten percent (10%) of total sales in that category of food product. Accordingly, there is a great disparity between the total sales percentages for the most popular twenty percent (20%) of the packaged food products and the least popular fifty to sixty percent (50%-60%)of the packaged food products.
A representative sales curve can be seen in FIG. 32 which shows the cumulative percent of total supermarket chain sales volume for a geographic area for the cumulative percent of total items available in that supermarket chain. In FIG. 32, the top twenty percent (20%) of products available in the supermarket comprise approximately seventy-five percent (75%) of the total supermarket sales volume. Further, the lower sixty percent (60%) of available products comprise approximately ten percent (10%) of the total sales volume. Because the top twenty percent (20%) of products will generate more profits than the lower (60%) of products, a retailer wants to focus upon a product assortment which emphasizes the top twenty percent (20%) of popular products while de-emphasizing (or eliminating) the lower sixty percent (60%) of the less popular products. In order to optimize a product selection, there is a need to identify the more popular top percentage of products and the lower less popular percentage of products.
Optimizing the profitability of your product assortment is even more important considering the limited shelf space available at grocery stores and supermarkets for each type of food product. As discussed in U. S. Pat. No. 4,112,598 to Maass and U.S. Pat. No. 4,947,322 to Temma, the layout of shelf space at retail stores can be modeled to optimize the shelf space as a limited resource. For the same amount of shelf space, the top percentage of popular food products will return greater profits than the least popular food products. Accordingly, grocery stores and supermarkets have a desire to maximize their profits from their finite amount of shelf space by allocating the most amount of space for the most popular food products and the least amount of space (or no space) for the least popular food products.
Computerized inventory systems are discussed in U. S. Patent No. 4,737,910 to Kimbrow, U.S. Pat. No. 4,797,819 to Dechirot, U.S. Pat. No. 4,972,318 to Brown, U.S. Pat. No. 4,783,740 to Ishizawa, U.S. Pat. No. 4,654,800 to Hayashi and U.S. Pat. No. 4,639,875 to Abraham. While these inventory systems track inventory levels and determine replenishment requirements for a product based upon sales data, these systems do not assist with the determination of the popular variety of goods or the optimization of product selection to maximize profits.
U.S. Pat. No. 5,237,496 to Kagimi, U.S. Pat. No. 5,128,861 to Kagimi, and U.S. Pat. No. 5,313,392 to Temma attempt to forecast future replenishment needs based upon planned sales and/or actual sales of products in inventory. While the products in inventory can be analyzed vis-a-vis their predicted sales or the sales of other goods in inventory, these patents only focus on the products already in inventory. Other popular products may exist in the market which are not carried in inventory, and if added to the store""s assortment, would increase the profitability for that store. By looking beyond an actual store""s current inventory to other stores in a chain or all stores in a geographic area, a greater number of the popular products and the less popular products can be identified for the geographic area or a chain of stores. A significant need exists with determining the popular products for all stores in a geographic region or for a chain of stores.
Retail sales data is available for most stores in a geographic region and for specific chains of stores in a geographic region, but the volume of this data makes it difficult to analyze in a quick and comprehensible manner. As such, a further problem exists with the ability to flexibly categorize products based on retail sales data and manipulate this large amount of retail sales data in a rapid manner to produce easy to understand tables and graphs which demonstrate the popularity of a variety of a food item and the optimal assortment of food items for a store. Accordingly, there is a significant need to analyze large amounts of retail sales data in a rapid manner, focus upon certain flexibly-defined categories of products in an assortment analysis, accurately identify the success or failure of a product assortment to carry the most popular revenue generating products, accurately identify the popularity of products (from most to least popular) for the geographic market or a chain of stores, and predict how changes in the assortment of products carried by the store would affect the revenues of a store.
It is therefore an object of the present invention to provide retail stores in general, and grocery and supermarket stores in particular, with a system capable of analyzing large amounts of retail sales data for all products and all manufacturers in a given product category sold in a geographic market and/or a chain of stores in a quick manner. Types of data analyzed by the present system include:
Chain Dataxe2x80x94for a given product category, this data would reflect sales of items from all producers for a store or chain of stores.
Market Dataxe2x80x94for a given product category, this data would reflect sales of items from all producers for a given market (e.g. geographical). This data can be combined to show how a chain in a specific market is performing relative to the entire market.
Another object of the present invention is to allow the system user to flexibly create and revise categories of products based upon criteria selected by the user including product name, food type, flavoring, package size and package type. Categorization of products is defined by category segment where a product category is a general description of product and a product segment is a particular description of a product. For instance, salty snacks would be a general product category, while pretzels or potato chips would be a particular product segment of the salty snacks category. Other useful categories, such as all salty snack foods or all warehouse container products (e.g.., tins, canisters) can be used in the analysis to determine what the optimum variety of products should be carried in a particular chain of stores and/or geographic area.
Further, it is an object of the present invention to determine how a store""s present assortment of products is succeeding or failing to cover the market demand with the items carried by the store. In analyzing the revenue-generating capability of an assortment of products, the following terms are used:
Percent Product Coveragexe2x80x94the items carried by a chain in a product segment divided by the total number of items in the market for that product segment. The percent product coverage will indicate how many items are carried by the chain versus the total number of items available in the market. For instance, a 100% product coverage for a chain would mean all the available products in that product segment are carried by the chain, whereas a 50% product coverage would mean the one half of the available products in that product segment are carried by the chain.
Percent Market Demandxe2x80x94the market sales for items carried by a chain divided by the total market sales for all items in a product segment. The percent market demand will show how many of the total sales in the market for all items in a segment are covered (by percentage) by the items carried by the chain. For instance, a 100% market demand for items carried by a chain would mean that all the market sales for products in that segment are covered by the items carried by a chain, while a 75% market demand for items carried by a chain would mean that the market sales for products carried by the chain cover only three quarters of the total market sales for all products in that segment.
Market Coveragexe2x80x94using the percentage product coverage and the percentage market demand, the market coverage would indicate how much of the total sales for products in the market for a segment are being covered by the product selection carried by the chain. If a chain has 50% product coverage and a 92% market demand, the store""s product coverage is said to be 92% of the revenue in the market is covered by the store""s assortment of only one-half (50%) of the available products in the market. This indicates the store has selected a fairly popular variety of items compared to the total number of items available in that market. This analysis answers the question xe2x80x9cIs this store/chain carrying the correct assortment of popular items?xe2x80x9d
Yet another object of the present invention is to identify the more popular and least popular products by defined categories thereby facilitating assortment management optimization. Because a better assortment of more popular items translates into more profits, optimization of the more popular products for a given shelf space should maximize profits. The present system can classify the products in the market or carried by a chain into classes of products depending on their popularity. The following term is used to define this popularity class:
Velocity Classxe2x80x94a measurement of the average throughput of an item sold by a store handling that item over a certain time period. This classification answers the question xe2x80x9cfor each item in that chain, what is average unit weekly sales per store handling that item?xe2x80x9d For example, a first product may have an average throughput of 6 units per week per store handling that item and a second product may have an average throughput of 33 units per week per store handling. On a scale of 1 to 7 (1 being most popular and 7 being least popular), the velocity class of the first product may be 5 and the velocity class of the second product may be 1. In this example, this type of analysis would provide the store with an easy to understand classifications for describing products in an assortment with average throughput values, such as velocity classes.
Another object of the present invention is to identify the best assortment or variety of popular products based upon an analysis of retail sales data for all stores in a particular geographic region or a chain of stores in a geographic area. Assortment planning analysis, also called xe2x80x9cwhat if analysisxe2x80x9d evaluates items based upon the designated velocity class to identify slow moving items and fast moving items, and determine if there are items which can be added to or deleted from an assortment to increase revenues. Assortment planning analysis includes conducting hypothetical analysis into revenue generation based upon adding or deleting products from an assortment carried by a store or chain. For example, by eliminating some of the products with high velocity classifications (less popular) and adding some of the products with low velocity classifications (high popularity), a store may expect a significant shift in revenues.
Another object of the invention is to produce quality graphical and tabular outputs for the market coverage analysis, the velocity class analysis, and the assortment planning analysis. Tabular outputs may include general or detailed information, and the graphical information may be presented in a pie chart or in a bar chart format. Presentation quality charts and tables are capable of being automatically generated by the system for overhead or notebook presentations.
Overall, the present invention is capable of analyzing large amounts of retail sales data by reformatting and manipulating the sales data in an automated manner to analyze the market coverage of store""s present assortment, identify the most and least popular products in the market, determine how changes in inventory can effect the revenue generated by a store, provide recommendations on more profitable varieties of products the store could be carrying on its shelf space, and produce easy to understand graphical and tabular output charts supporting the analysis.
While the present invention is described in the preferred embodiment with respect to the package food product market, the invention is capable of use in any general retail sales analysis to flexibly categorize products, determine the popularity of products in a market and conduct analysis to optimize the assortment of products carried by a store or chain of stores. Additional objects, advantages, and other novel features of the invention will be set forth in part in the description that follows and in part will become apparent to those skilled in the art upon examination of the following or may be learned with the practice of the invention. The objects and advantages of the invention may be realized and attained by means of the invention particularly covered by the appended claims.